Hybrid approach for financial forecasting with support vector machines

Financial markets are the biggest business platforms in the world. Therefore, financial forecasting is getting a lot of attention in today’s economic context. Accurate forecast is beneficial to broker firms, governments, individuals etc. Vast range of forecasting methods, models have introduced by the research community. However, the risk involved with trading on those markets are very high. Such complexity makes a difficulty of making consistent profit. Building an accurate forecasting model is still an active and interesting research area for the academic community. Recently, nonlinear statistical models such as neural network, support vector machine have shown greater capability to forecast financial markets over conventional methods. This dissertation pro-posed a hybrid support vector machine model which consists of wavelet transform and k-means clustering for foreign exchange market forecasting. The proposed model analyzes the trends and makes a forecast by entirely depending on the past exchange data. Wavelet transform is used to remove the noise of the time series. K-means clustering cluster the input space according to the similarities of the input vectors and finally support vector models make a forecast for the relevant cluster. The proposed hybrid forecasting system was tested on real market environment to check the fore-casting capability. Auto trading algorithm developed on ‘metatrader4’ platform used the forecast of the model to trade on the real conditions. Results confirmed that the proposed model can fore-cast price movements with greater accuracy that leads to profitable trades on foreign exchange marke